Abstract
Motivated by the inconvenience or even inability to explain the mathematics of the state space optimization of finite state machines (FSMs) in most existing results, we consider the problem by viewing FSMs as logical dynamic systems. Borrowing ideas from the concept of equilibrium points of dynamic systems in control theory, the concepts of t-equivalent states and t-source equivalent states are introduced. Based on the state transition dynamic equations of FSMs proposed in recent years, several mathematical formulations of t-equivalent states and t-source equivalent states are proposed. These can be analogized to the necessary and sufficient conditions of equilibrium points of dynamic systems in control theory and thus give a mathematical explanation of the optimization problem. Using these mathematical formulations, two methods are designed to find all the t-equivalent states and t-source equivalent states of FSMs. Further, two ways of reducing the state space of FSMs are found. These can be implemented without computers but with only pen and paper in a mathematical manner. In addition, an open question is raised which can further improve these methods into unattended ones. Finally, the correctness and effectiveness of the proposed methods are verified by a practical language model.
摘要
现有大多数关于有限状态自动机 (finite state machines, FSM) 状态空间的优化方法不便甚至不能给出优化的数学意义. 本文将FSM视为逻辑动态系统, 借鉴控制论中动态系统平衡点的概念, 引入t-等价状态和t-源等价状态概念. 基于近年提出的FSM状态转移动力学方程, 得到t-等价状态和t-源等价状态的数学描述 (该数学描述可类比于控制论中关于动态系统平衡点的充要条件), 进而给出该优化问题的数学解释. 基于这些数学描述, 设计了求解FSM所有t-等价状态和t-源等价状态的两种方法. 此外, 找到降低FSM状态空间的两种路径. 可不借助计算机, 仅用纸笔以数学推演方式实现. 并且, 为使所设计的方法借助计算机能完全以无人值守方式运行, 提出一个开放性问题. 最后, 采用实际语言模型验证了结论的正确性和有效性.
Similar content being viewed by others
References
Chen WY, 2007. The Theory of Finite Automata. University of Electronic Science and Technology of China Press, Chengdu, China (in Chinese).
Cheng DZ, Qi HS, Zhao Y, 2012. An Introduction to Semi-tensor Product of Matrices and Its Applications. World Scientific, Singapore.
Chu D, Spinney RE, 2018. A thermodynamically consistent model of finite-state machines. Interf Focus, 8(6): 20180037. https://doi.org/10.1098/rsfs.2018.0037
Dahmoune M, El Abdalaoui EH, Ziadi D, 2014. On the transition reduction problem for finite automata. Fundam Inform, 132(1):79–94. https://doi.org/10.3233/fi-2014-1033
de Parga MV, García P, López D, 2013. A polynomial double reversal minimization algorithm for deterministic finite automata. Theor Comput Sci, 487:17–22. https://doi.org/10.1016/j.tcs.2013.03.005
Gao N, Han XG, Chen ZQ, et al., 2017. A novel matrix approach to observability analysis of finite automata. Int J Syst Sci, 48(16):3558–3568.
García P, López D, de Parga MV, 2014. Efficient deterministic finite automata split-minimization derived from Brzozowski’s algorithm. Int J Found Comput Sci, 25(6): 679–696. https://doi.org/10.1142/s0129054114500282
Gören S, Ferguson FJ, 2002. Chesmin: a heuristic for state reduction in incompletely specified finite state machines. Proc Design, Automation and Test in Europe Conf and Exhibition, p.248–254. https://doi.org/10.1109/DATE.2002.998280.
Gören S, Ferguson FJ, 2007. On state reduction of incompletely specified finite state machines. Comput Electr Eng, 33(1):58–69. https://doi.org/10.1016/j.compeleceng.2006.06.001
Grzes TN, Solov’ev VV, 2015. Minimization of power consumption of finite state machines by splitting their internal states. J Comput Syst Sci Int, 54(3):367–374. https://doi.org/10.1134/s1064230715030090
Han XG, Chen ZQ, 2018. A matrix-based approach to verifying stability and synthesizing optimal stabilizing controllers for finite-state automata. J Franklin Inst, 355(17):8642–8663. https://doi.org/10.1016/j.jfranklin.2018.09.009
Han XG, Chen ZQ, Liu ZX, et al., 2018. The detection and stabilisation of limit cycle for deterministic finite automata. Int J Contr, 91(4):874–886. https://doi.org/10.1080/00207179.2017.1295319
He YS, Yu ZH, Jian L, et al., 2019. Fault correction of algorithm implementation for intelligentized robotic multipass welding process based on finite state machines. Robot Comput-Int Manuf, 59:28–35. https://doi.org/10.1016/j.rcim.2019.03.002
Kamble SD, Thakur NV, Bajaj PR, 2018. Fractal coding based video compression using weighted finite automata. Int J Amb Comput Intell, 9(1):115–133. https://doi.org/10.4018/IJACI.2018010107
Klimowicz AS, Solov’ev VV, 2013. Minimization of incompletely specified Mealy finite-state machines by merging two internal states. J Comput Syst Sci Int, 52(3): 400–409. https://doi.org/10.1134/s106423071303009x
Li YM, Pedrycz W, 2007. Minimization of lattice finite automata and its application to the decomposition of lattice languages. Fuzzy Sets Syst, 158(13):1423–1436. https://doi.org/10.1016/j.fss.2007.03.003
Liu DS, Huang ZP, Zhang YM, et al., 2016. Efficient deterministic finite automata minimization based on backward depth information. PLoS ONE, 11(11): e0165864. https://doi.org/10.1371/journal.pone.0165864
Lu JQ, Li HT, Liu Y, et al., 2017. Survey on semi-tensor product method with its applications in logical networks and other finite-valued systems. IET Contr Theory Appl, 11(13):2040–2047.
Martinek P, 2018. Some notes to minimization of multiset finite automata. AIP Conf Proc, 1978(1):470019. https://doi.org/10.1063/L5044089
Melnikov B, 2010. Once more on the edge-minimization of nondeterministic finite automata and the connected problems. Fundam Inform, 104(3):267–283. https://doi.org/10.3233/fi-2010-349
Perkowski MA, Jóźwiak L, Zhao W, 2001. Symbolic two-dimensional minimization of strongly unspecified finite state machines. J Syst Archit, 47(1):15–28. https://doi.org/10.1016/s1383-7621(00)00038-2
Solov’ev VV, 2010. Minimization of Moore finite automata by internal state gluing. J Commun Technol Electron, 55(5):584–592. https://doi.org/10.1134/s1064226910050153
Solov’ev VV, 2011. Minimization of Mealy finite state machines via internal state merging. J Commun Technol Electron, 56(2):207–213. https://doi.org/10.1134/s1064226911020136
Solov’ev VV, 2014. Complex minimization method for finite state machines implemented on programmable logic devices. J Comput Syst Sci Int, 53(2):186–194. https://doi.org/10.1134/s1064230714020154
Solov’ev VV, 2017. Minimization of Mealy finite-state machines by using the values of the output variables for state assignment. J Comput Syst Sci Int, 56(1):96–104. https://doi.org/10.1134/s1064230717010129
Voeten C, van Zaanen M, 2018. The influence of context on the learning of metrical stress systems using finite-state machines. Comput Ling, 44(2):329–348. https://doi.org/10.1162/COLI_a_00317
Wang YB, Li YM, 2018. Minimization of lattice multiset finite automata. J Intell Fuzzy Syst, 35(1):627–637. https://doi.org/10.3233/jifs-161382
Xu XR, Hong YG, 2012. Matrix expression and reachability analysis of finite automata. J Contr Theory Appl, 10(2): 210–215. https://doi.org/10.1007/s11768-012-1178-4
Yan YY, Chen ZQ, Liu ZX, 2015. Semi-tensor product approach to controllability and stabilizability of finite automata. J Syst Eng Electron, 26(1):134–141. https://doi.org/10.1109/jsee.2015.00018
Yan YY, Chen ZQ, Yue JM, et al., 2016. STP approach to model controlled automata with application to reachability analysis of DEDS. Asian J Contr, 18(6): 2027–2036. https://doi.org/10.1002/asjc.1294
Yan YY, Yue JM, Fu ZM, et al., 2019a. Algebraic criteria for finite automata understanding of regular language. Front Comput Sci, 13(5):1148–1150. https://doi.org/10.1007/s11704-019-6525-x
Yan YY, Yue JM, Chen ZQ, et al., 2019b. Algebraic expression and construction of control sets of graphs using semi-tensor product of matrices. IEEE Access, 7:113440–113451. https://doi.org/10.1109/access.2019.2935321
Yan YY, Yue JM, Chen ZQ, 2019c. Algebraic method of simplifying Boolean networks using semi-tensor product of matrices. Asian J Contr, 21(6):2569–2577. https://doi.org/10.1002/asjc.2125
Yue JM, Yan YY, 2019. Exponentiation representation of Boolean matrices in the framework of semi-tensor product of matrices. IEEE Access, 7:153819–153828. https://doi.org/10.1109/ACCESS.2019.2948357
Yue JM, Yan YY, Chen ZQ, et al., 2019a. Identification of predictors of Boolean networks from observed attractor states. Math Method Appl Sci, 42(11):3848–3864. https://doi.org/10.1002/mma.5616
Yue JM, Yan YY, Chen ZQ, 2019b. Language acceptability of finite automata based on theory of semi-tensor product of matrices. Asian J Contr, 21(6):2634–2643. https://doi.org/10.1002/asjc.2190
Yue JM, Yan YY, Chen ZQ, 2020a. Matrix approach to simplification of finite state machines using semi-tensor product of matrices. Asian J Contr, 22(5):2061–2070. https://doi.org/10.1002/asjc.2123
Yue JM, Yan YY, Chen ZQ, 2020b. Three matrix conditions for the reduction of finite automata based on the theory of semi-tensor product of matrices. Sci China Inform Sci, 63(2):129203. https://doi.org/10.1007/s11432-018-9739-9
Zhang KZ, Zhang LJ, 2016. Observability of Boolean control networks: a unified approach based on finite automata. IEEE Trans Autom Contr, 61(9):2733–2738. https://doi.org/10.1109/tac.2015.2501365
Zhang QL, Feng JE, Yan YY, 2020. Finite-time pinning stabilization of Markovian jump Boolean networks. J Franklin Inst, 357(11):7020–7036. https://doi.org/10.1016/j.jfranklin.2020.05.010
Zhang ZP, Chen ZQ, Han XG, et al., 2017. Static output feedback stabilization of deterministic finite automata. Proc 36th Chinese Control Conf, p.2421–2425. https://doi.org/10.23919/ChiCC.2017.8027721
Zhang ZP, Chen ZQ, Liu ZX, 2018a. Modeling and reachability of probabilistic finite automata based on semi-tensor product of matrices. Sci China Inform Sci, 61(12):129202. https://doi.org/10.1007/s11432-018-9507-7
Zhang ZP, Chen ZQ, Han XG, et al., 2018b. On the static output feedback stabilization of deterministic finite automata based upon the approach of semi-tensor product of matrix. Kybernetika, 54(1):41–60. https://doi.org/10.14736/kyb-2018-1-0041
Acknowledgements
The authors would like to express their sincere thanks for the advice given by Assistant Prof. Xiangru XU and Prof. Yiguang HONG.
Author information
Authors and Affiliations
Contributions
Jumei YUE raised the research questions and the ideas to solve them. Yongyi YAN and Zengqiang CHEN guided Jumei YUE and He DENG to carry out the research in the whole process. Jumei YUE drafted the manuscript. He DENG helped organize the manuscript. Yongyi YAN and Zengqiang CHEN revised and finalized the paper.
Corresponding author
Ethics declarations
Jumei YUE, Yongyi YAN, Zengqiang CHEN, and He DENG declare that they have no conflict of interest.
Additional information
Project supported by the National Natural Science Foundation of China (Nos. U1804150, 62073124, and 61973175)
Rights and permissions
About this article
Cite this article
Yue, J., Yan, Y., Chen, Z. et al. State space optimization of finite state machines from the viewpoint of control theory. Front Inform Technol Electron Eng 22, 1598–1609 (2021). https://doi.org/10.1631/FITEE.2000608
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2000608
Key words
- Finite state machines
- Finite-valued systems
- Logical systems
- Logical networks
- Semi-tensor product of matrices
- Space optimization